Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method of tuning a cloud service, comprising: detecting an event and an event type on a cloud server application by monitoring at least one of a hardware value and a software value on a cloud server; determining at least one exposed application parameter to tune based on the event type; determining a tuning priority of the at least one application parameter based on historical tuning data; automatically tuning the at least one application parameter by modifying a software variable on the cloud server; and in the event that tuning the at least one application parameter resolves the event type, increasing the tuning priority of the at least one application parameter, and in the event that tuning the at least one application parameter fails to resolve the event type, decreasing the priority of the at least one application parameter, rolling back the tuning of the at least one application parameter, and transmitting an instruction to invoke a default tune setting.
This invention relates to cloud computing and addresses the problem of automatically optimizing cloud service performance and stability in response to detected events. The method involves monitoring a cloud server application to detect an event and its type. This detection is achieved by observing at least one hardware or software value on the cloud server. Based on the identified event type, the system determines which application parameters are exposed and suitable for tuning. A tuning priority for these parameters is then established, drawing upon historical tuning data to inform this decision. The core of the method is the automatic tuning of the selected application parameter(s) by modifying a software variable on the cloud server. The system then assesses the outcome of this tuning. If the tuning successfully resolves the detected event type, the priority of the tuned parameter is increased for future similar events. Conversely, if the tuning fails to resolve the event type, the priority of that parameter is decreased. In such failure cases, the tuning modification is rolled back to its previous state, and an instruction is sent to activate a default tune setting.
2. The method according to claim 1 , wherein automatically tuning the at least one application parameter further comprises looping through an order of priority.
This invention relates to a method for automatically tuning application parameters in a computing system to optimize performance. The method addresses the challenge of efficiently adjusting multiple application parameters to achieve desired performance outcomes without manual intervention. The system monitors performance metrics of an application and automatically adjusts at least one parameter based on predefined criteria. The tuning process involves looping through a predefined order of priority for the parameters, ensuring that higher-priority parameters are adjusted before lower-priority ones. This prioritization helps maintain system stability and performance by focusing on the most critical parameters first. The method may also include dynamically updating the priority order based on real-time performance data, allowing the system to adapt to changing conditions. The invention is particularly useful in environments where manual tuning is impractical or inefficient, such as large-scale distributed systems or real-time applications. By automating the tuning process and incorporating a priority-based approach, the method ensures that performance optimizations are applied in a structured and efficient manner.
3. The method according to claim 1 , wherein the event type comprises a performance degradation alert.
A system monitors computing resources to detect performance degradation alerts, which indicate when system performance falls below predefined thresholds. The system identifies the specific event type, such as a performance degradation alert, and triggers automated responses to mitigate the issue. These responses may include scaling resources, rerouting traffic, or initiating diagnostic procedures. The system continuously evaluates performance metrics to ensure optimal operation and proactively addresses potential failures before they escalate. By automating the detection and response to performance degradation, the system reduces downtime and improves overall system reliability. The method involves collecting performance data, analyzing it to detect anomalies, and applying predefined corrective actions based on the detected event type. This approach ensures that performance issues are resolved efficiently, minimizing disruptions to users and maintaining service quality. The system may also log events for further analysis and reporting, enabling continuous improvement of performance monitoring and response strategies.
4. The method according to claim 1 , wherein the event type comprises a threshold level alert.
A system and method for monitoring and processing event data in a computing environment, particularly for detecting and responding to threshold-level alerts. The invention addresses the challenge of efficiently identifying and managing critical events in real-time data streams, where excessive or irrelevant alerts can overwhelm monitoring systems and lead to missed critical issues. The method involves receiving event data from one or more sources, analyzing the data to determine its type, and processing it based on predefined criteria. A key aspect is the detection of threshold-level alerts, which are events that exceed or fall below specified thresholds, indicating potential system anomalies or failures. When such an alert is detected, the system triggers an automated response, such as generating a notification, logging the event, or initiating corrective actions. The method may also include filtering events to reduce noise and prioritizing alerts based on severity or impact. The system can be applied in various domains, including network monitoring, industrial control systems, and cybersecurity, where timely detection and response to critical events are essential. The invention improves system reliability and operational efficiency by ensuring that threshold-level alerts are promptly identified and addressed.
5. The method according to claim 1 , wherein the at least one application parameter comprises at least one of a database connection size, a caching value, a connection pool thread value, and a worker threads value.
This invention relates to optimizing application performance by dynamically adjusting key configuration parameters in a computing system. The problem addressed is the inefficiency of static parameter settings in applications, which can lead to suboptimal resource utilization, performance bottlenecks, or excessive resource consumption. The solution involves dynamically configuring at least one application parameter to improve system efficiency. The parameters include database connection size, caching value, connection pool thread value, and worker threads value. These parameters control how the application interacts with databases, manages memory, and handles concurrent tasks. By dynamically adjusting these values based on system conditions, the invention ensures optimal performance without manual intervention. The method may involve monitoring system metrics, such as load or response times, and automatically tuning the parameters to balance resource usage and performance. This approach is particularly useful in environments with variable workloads, where static settings would either underutilize or overburden system resources. The dynamic adjustment of these parameters helps maintain consistent performance while adapting to changing demands.
6. The method according to claim 1 , wherein automatically tuning the application parameter comprises tuning the application parameter incrementally.
This invention relates to a method for optimizing application performance by automatically tuning application parameters in an incremental manner. The method addresses the challenge of efficiently adjusting application parameters to improve performance without causing instability or excessive resource consumption. The technique involves dynamically monitoring application behavior and making small, gradual adjustments to parameters to achieve optimal performance while minimizing disruptions. The method includes a system that continuously evaluates application performance metrics, such as response time, throughput, or resource utilization. Based on these metrics, the system identifies suboptimal parameter settings and applies incremental tuning adjustments. These adjustments are made in small steps to avoid sudden performance degradation or system instability. The system may use feedback loops to assess the impact of each adjustment and refine future tuning actions accordingly. The incremental tuning process ensures that parameter changes are applied in a controlled manner, allowing the system to stabilize after each adjustment before proceeding with further optimizations. This approach reduces the risk of over-tuning or under-tuning, leading to more reliable and consistent performance improvements. The method may be applied to various types of applications, including software systems, databases, or cloud-based services, where dynamic parameter tuning is beneficial for maintaining efficiency and responsiveness.
7. The method according to claim 1 , where modifying a software variable on the cloud server comprises at least one of increasing a variable, decreasing a variable, and toggling a variable.
This invention relates to cloud-based software systems where remote modification of software variables is required. The problem addressed is the need for efficient and flexible control of software variables stored on a cloud server, particularly in scenarios where variables must be adjusted dynamically without direct access to the server's underlying code or infrastructure. The method involves modifying a software variable stored on a cloud server by performing at least one of three operations: increasing the variable's value, decreasing the value, or toggling its state. These modifications are executed remotely, allowing for real-time adjustments without requiring physical access to the server or manual code changes. The system ensures that variable modifications are applied consistently and securely, maintaining system integrity while enabling dynamic configuration. The approach is particularly useful in applications where variables control system behavior, such as configuration settings, performance thresholds, or feature toggles. By supporting multiple modification operations, the method provides flexibility in how variables are adjusted, accommodating different use cases where variables may need to be incremented, decremented, or switched between states. The solution simplifies remote management of cloud-hosted software, reducing operational overhead and improving responsiveness to changing requirements.
8. The method according to claim 1 , wherein the default tune setting comprises an auto scaling function.
A method for optimizing audio performance in electronic devices addresses the challenge of manually adjusting audio settings to achieve desired sound quality across different environments. The method involves dynamically adjusting audio parameters based on real-time conditions, such as ambient noise levels or user preferences, to enhance listening experiences. A key feature is the inclusion of a default tune setting that incorporates an auto-scaling function. This function automatically scales audio output levels and equalization settings to maintain optimal sound quality without requiring user intervention. The auto-scaling function may analyze environmental factors, such as background noise, and adjust parameters like volume, bass, and treble to ensure clarity and balance. Additionally, the method may integrate user feedback or historical data to refine adjustments over time. By automating these adjustments, the method simplifies the user experience while improving audio performance in various scenarios, such as music playback, voice calls, or multimedia consumption. The solution is particularly useful in portable devices, smart speakers, and other audio systems where manual tuning is impractical or inconvenient.
9. A system for tuning a cloud service, the system comprising a processing resource in communication with a network interface, a computer readable medium, wherein the computer readable medium contains a set of instructions, and a processing unit to carry out a set of instructions to: detect, on the processing resource, a first monitoring event and an event type on a cloud server application; determine at least one application parameter exposed in the cloud server application; determine an order of priority for tuning the at least one application parameter based on the detected event type; tune the application parameter; detect, on the processing resource, a second monitoring event on the cloud server application; and determine whether the second monitoring event indicates that the first monitoring event has been resolved, wherein in the event that the first monitoring event is not resolved, the processing resource is to decrease the priority of the at least one application parameter, roll back the tuned application parameter to a previous state, and invoke an auto-scaling function.
This system addresses the challenge of dynamically optimizing cloud service performance by automatically tuning application parameters in response to detected events. The system monitors a cloud server application to identify performance issues, such as high latency or resource exhaustion, and adjusts configurable parameters to resolve them. It prioritizes tuning actions based on the type of event detected, ensuring critical issues are addressed first. After tuning, the system continues monitoring to verify if the event is resolved. If the issue persists, the system reduces the priority of the adjusted parameter, reverts it to a previous state, and triggers auto-scaling to allocate additional resources. The system operates through a processing resource connected to a network interface and a computer-readable medium containing executable instructions. The instructions enable the system to detect events, analyze application parameters, prioritize tuning actions, apply adjustments, and assess their effectiveness. This approach improves cloud service reliability and efficiency by automating parameter optimization and resource management in real-time.
10. The system according to claim 9 , wherein the first monitoring event comprises at least one of a CPU alert, a memory alert, a disk drive alert, a bandwidth alert, and an application alert.
A system monitors computing resources to detect performance issues in real-time. The system identifies and responds to various types of alerts, including CPU, memory, disk drive, bandwidth, and application alerts. Each alert indicates a potential problem, such as excessive resource usage, failures, or performance degradation. The system processes these alerts to trigger corrective actions, such as load balancing, resource allocation adjustments, or system maintenance. The monitoring is continuous, ensuring proactive detection and resolution of issues before they escalate. The system may also log alert data for analysis, enabling long-term performance optimization. This approach improves system reliability and efficiency by addressing problems dynamically and reducing downtime. The system is applicable to servers, cloud environments, and distributed computing systems where resource monitoring is critical.
11. The system according to claim 9 , wherein the previous state comprises a virtual machine image.
A system for managing virtual machine states in a computing environment addresses the challenge of efficiently storing and retrieving virtual machine configurations. The system includes a storage mechanism that captures and preserves the previous state of a virtual machine, which may be represented as a virtual machine image. This image contains the complete state of the virtual machine, including its operating system, applications, and data, allowing for rapid restoration or migration. The system also includes a processing unit that compares the previous state with a current state to identify differences, enabling selective updates or rollbacks. Additionally, the system may include a network interface for transmitting the virtual machine image to a remote location, facilitating disaster recovery or load balancing. The storage mechanism ensures that the virtual machine image is stored in a compressed or encrypted format to optimize storage efficiency and security. The system may also include a user interface for managing state transitions, allowing users to initiate state captures, restorations, or comparisons. This approach enhances system reliability, reduces downtime, and simplifies virtual machine management by providing a structured method for handling state transitions.
12. The system according to claim 9 , wherein the instructions are to detect, on the processing resource, the second monitoring event on the cloud server application at a pre-set time after the application parameter is tuned.
A system monitors and adjusts application performance in cloud computing environments. The system detects performance issues by tracking application parameters such as response time, throughput, or resource utilization. When a parameter deviates from a predefined threshold, the system automatically tunes the parameter to optimize performance. The system also monitors the effectiveness of the tuning by detecting a second monitoring event on the cloud server application at a pre-set time after the tuning is applied. This allows the system to verify whether the tuning action resolved the performance issue or if further adjustments are needed. The system may use machine learning models to predict optimal tuning parameters based on historical performance data. The monitoring and tuning processes are automated, reducing manual intervention and improving system reliability. The system is designed to work with various cloud-based applications, ensuring consistent performance across different workloads. The pre-set time for detecting the second monitoring event ensures that the system evaluates the impact of tuning actions within a controlled timeframe, preventing unnecessary adjustments. This approach enhances application stability and efficiency in dynamic cloud environments.
13. A non-transitory computer readable storage medium on which is embedded a computer program, said computer program to deploy a tunable cloud services application, said computer program comprising a set of instructions to: associate at least one tunable application parameter with at least one event type; expose the at least one tunable application parameter to a production cloud server for auto-remediation on the production cloud server; set an adjustable priority level for the at least one application parameter; set a rollback point for the at least one application parameter; set a monitor threshold for the at least one event type; set a default tune action for the at least one application parameter; and deploy the tunable cloud services application to the production cloud server.
This invention relates to cloud computing systems and addresses the challenge of dynamically optimizing cloud services applications in production environments. The system provides a tunable cloud services application that automatically adjusts performance based on real-time conditions. The application includes a set of tunable parameters linked to specific event types, allowing the system to detect and respond to changes in the cloud environment. These parameters are exposed to a production cloud server, enabling auto-remediation without manual intervention. Each parameter has an adjustable priority level to determine its importance in decision-making, a rollback point to revert to a previous state if issues arise, and a monitor threshold to trigger actions when certain conditions are met. Additionally, a default tune action is predefined for each parameter to ensure consistent behavior. The application is deployed to the production cloud server, where it continuously monitors and adjusts settings to maintain optimal performance. This approach reduces downtime and improves efficiency by automating adjustments in response to dynamic workloads and system events.
14. The computer readable storage medium according to claim 13 , wherein the event type is based on an application response time.
A system monitors and analyzes application performance by tracking event types, including application response times, to detect anomalies or performance degradation. The system collects performance data from applications running on computing devices, processes this data to identify patterns or deviations, and generates alerts or triggers corrective actions when predefined thresholds are exceeded. The event type classification is based on application response times, allowing the system to distinguish between normal and abnormal performance behavior. By continuously evaluating response times, the system can proactively identify bottlenecks, latency issues, or other inefficiencies in application execution. The system may also correlate response time data with other performance metrics, such as resource utilization or error rates, to provide a comprehensive view of application health. This approach enables early detection of potential failures or performance degradation, improving system reliability and user experience. The system can be deployed in cloud environments, on-premises infrastructure, or hybrid setups, adapting to different application architectures and workloads. The use of response time as an event type allows for precise performance monitoring and adaptive responses to dynamic conditions.
15. The computer readable storage medium according to claim 13 , wherein the default tune action comprises an instruction to increase resources available to the production cloud server.
This invention relates to cloud computing resource management, specifically addressing the challenge of dynamically adjusting resources in a production cloud server environment to maintain performance and reliability. The system monitors the production cloud server for performance metrics such as latency, throughput, or error rates. When performance degradation is detected, the system automatically triggers a default tuning action to mitigate the issue. The default tuning action involves increasing the resources allocated to the production cloud server, such as CPU, memory, or network bandwidth. The system may also log the performance metrics and the applied tuning action for future analysis. Additionally, the system can compare the performance metrics before and after the tuning action to assess its effectiveness. If the tuning action fails to resolve the performance issue, the system may escalate the problem to an administrator or apply alternative tuning actions. The invention ensures that cloud servers maintain optimal performance by proactively adjusting resources based on real-time monitoring and automated corrective measures.
Unknown
October 1, 2019
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